In the modern age of wireless communications, it is difficult to ensure that networks are secured from surveillance, intrusion, or manipulation. Various devices and systems have been proposed for detecting when a communication channel has been compromised. Some systems use rules-based engines which analyze security events generated by network devices, where the security events are aggregated and analyzed to detect intrusion. Other systems have used threat scores derived from threat feeds to represents the severity of network intrusion.
In these conventional systems, detection of a network being compromised involves processing and analyzing very large quantities of data received from a large number of devices. The various devices transmit the data to a centralized server or comparable service-level platform having a high-powered processor for high-performance processing, where the processing and analysis can be carried out. However, even high-powered processing devices are subject to errors, slow processing or bottlenecks, and failure during processing which is computationally expensive or processor hungry, especially as the number of individual devices sending data to be analyzed increases to scale. Thus, even a centralized computing device equipped with the highest processor performance possible is susceptible to errors, slow processing, and failures in due time as the user base grows.
Thus, a heretofore unaddressed need exists in the industry to address the aforementioned deficiencies and inadequacies.